Apriori
Warren Wang
set environment
EDA
data structure transforming
summary(bf)## User_ID Product_ID Gender
## Min. :1000001 Length:537577 Length:537577
## 1st Qu.:1001495 Class :character Class :character
## Median :1003031 Mode :character Mode :character
## Mean :1002992
## 3rd Qu.:1004417
## Max. :1006040
##
## Age Occupation City_Category
## Length:537577 Min. : 0.000 Length:537577
## Class :character 1st Qu.: 2.000 Class :character
## Mode :character Median : 7.000 Mode :character
## Mean : 8.083
## 3rd Qu.:14.000
## Max. :20.000
##
## Stay_In_Current_City_Years Marital_Status Product_Category_1
## Length:537577 Min. :0.0000 Min. : 1.000
## Class :character 1st Qu.:0.0000 1st Qu.: 1.000
## Mode :character Median :0.0000 Median : 5.000
## Mean :0.4088 Mean : 5.296
## 3rd Qu.:1.0000 3rd Qu.: 8.000
## Max. :1.0000 Max. :18.000
##
## Product_Category_2 Product_Category_3 Purchase
## Min. : 2.00 Min. : 3.0 Min. : 185
## 1st Qu.: 5.00 1st Qu.: 9.0 1st Qu.: 5866
## Median : 9.00 Median :14.0 Median : 8062
## Mean : 9.84 Mean :12.7 Mean : 9334
## 3rd Qu.:15.00 3rd Qu.:16.0 3rd Qu.:12073
## Max. :18.00 Max. :18.0 Max. :23961
## NA's :166986 NA's :373299head(bf)str(bf)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 537577 obs. of 12 variables:
## $ User_ID : num 1e+06 1e+06 1e+06 1e+06 1e+06 ...
## $ Product_ID : chr "P00069042" "P00248942" "P00087842" "P00085442" ...
## $ Gender : chr "F" "F" "F" "F" ...
## $ Age : chr "0-17" "0-17" "0-17" "0-17" ...
## $ Occupation : num 10 10 10 10 16 15 7 7 7 20 ...
## $ City_Category : chr "A" "A" "A" "A" ...
## $ Stay_In_Current_City_Years: chr "2" "2" "2" "2" ...
## $ Marital_Status : num 0 0 0 0 0 0 1 1 1 1 ...
## $ Product_Category_1 : num 3 1 12 12 8 1 1 1 1 8 ...
## $ Product_Category_2 : num NA 6 NA 14 NA 2 8 15 16 NA ...
## $ Product_Category_3 : num NA 14 NA NA NA NA 17 NA NA NA ...
## $ Purchase : num 8370 15200 1422 1057 7969 ...
## - attr(*, "spec")=
## .. cols(
## .. User_ID = col_double(),
## .. Product_ID = col_character(),
## .. Gender = col_character(),
## .. Age = col_character(),
## .. Occupation = col_double(),
## .. City_Category = col_character(),
## .. Stay_In_Current_City_Years = col_character(),
## .. Marital_Status = col_double(),
## .. Product_Category_1 = col_double(),
## .. Product_Category_2 = col_double(),
## .. Product_Category_3 = col_double(),
## .. Purchase = col_double()
## .. )# User_ID: Unique identifier of shopper.
# Product_ID: Unique identifier of product. (No key given)
# Gender: Sex of shopper.
# Age: Age of shopper split into bins.
# Occupation: Occupation of shopper. (No key given)
# City_Category: Residence location of shopper. (No key given)
# Stay_In_Current_City_Years: Number of years stay in current city.
# Marital_Status: Marital status of shopper.
# Product_Category_1: Product category of purchase.
# Product_Category_2: Product may belong to other category.
# Product_Category_3: Product may belong to other category.
# Purchase: Purchase amount in dollars.
##Below are my steps for doing this project:
##1. Finishing EDA on every variable, getting information, comming out proper actions.
##2. When doing EDA, transferring every variable into right datatype
##3. Feature Engeering
##4. ModellingGender
gender <- bf%>%select(User_ID,Gender)%>%group_by(User_ID)%>%distinct()
summary(gender)## User_ID Gender
## Min. :1000001 Length:5891
## 1st Qu.:1001518 Class :character
## Median :1003026 Mode :character
## Mean :1003025
## 3rd Qu.:1004532
## Max. :1006040##info: there are 5891 distinct customers
ggplot(gender)+
geom_bar(aes(x=Gender,y=..count.., fill=Gender))+
labs(title='Gender of Customers')+
scale_fill_brewer(palette='PuBuGn')
sum(gender$Gender=='M')/nrow(gender)## [1] 0.7171957##info: there are 71.7% of cutomers are Males
##actn: take into account males' buying behavior
##However, maybe Females have stornger buying power?
##Q: whether females spend more?
genderSpend <- bf%>%select(User_ID, Gender, Purchase)%>%group_by(User_ID,Gender)%>% summarise(totalSpending=sum(Purchase),avgSpending=mean(Purchase))
ggplot(genderSpend, aes(totalSpending))+
geom_histogram(bins = 200)+
scale_x_continuous(labels=comma)+
coord_cartesian(xlim=c(0,5000000))+
facet_wrap(~Gender)+
labs(title='Total Spending distribution devided by Gender')
ggplot(genderSpend, aes(avgSpending))+
geom_histogram(bins=100)+
facet_wrap(~Gender)+
labs(title='Average Spending distribution devided by Gender')
ggplot(genderSpend,aes(Gender, totalSpending))+
geom_bar(stat='summary', fun.y='mean', fill='gold2')+
labs(title='TotalSpending mean devided by Gender')+
geom_hline(yintercept=median(genderSpend$totalSpending[genderSpend$Gender=='F']), linetype='dashed', color='red')+
geom_hline(yintercept=median(genderSpend$totalSpending[genderSpend$Gender!='F']), linetype='dashed', color='blue')
ggplot(genderSpend,aes(Gender, totalSpending))+
geom_bar(stat='summary',fun.y='median')+
labs(title='TotalSpending median devided by Gender')+
scale_y_continuous(labels=comma)
##info: Males spend more, on average and on median. (F medain=398178, M median=565925; F mean= 699054,M mean=911963.2)
##info: Distribution(right skewed) says that both genders have some super shoppers (outliers spending very much)
##actn: Figuring out what super shoppers buy and what most of people buyTop Sellers
topSeller <- bf%>%select(Product_ID)%>%group_by(Product_ID)%>%summarise(count=n())%>%arrange(desc(count))
sum(topSeller$count[1:5])/nrow(bf)## [1] 0.01488903##info: 3,623 products in Total
##info: Top 5 Selling product:P00265242,P00110742,P00025442,P00112142,P00057642 consisting 1.5% in total buying times
top5 <- bf[bf$Product_ID==topSeller$Product_ID[1:5],]%>% arrange(Product_ID)
##Q: if gender play a role
top5WithGender <- top5 %>% group_by(Product_ID,Gender)%>%summarise(count=n())
sum(top5WithGender$count[top5WithGender$Gender=='M'&top5WithGender$Product_ID!='P00265242'])/sum(top5WithGender$count[top5WithGender$Product_ID!='P00265242'])## [1] 0.7963405##info: if we uncount P00265242, Males consist more purchase of other top 4 seller.
##79.6% of purchase of these 4 products are for males, while for whole dataset, males contribute 71.7%. This conclusion makes sense, since the majority of customers are males, and what they like are more likely become top seller.Age
cust_Age <- bf%>%select(User_ID,Age,Gender,City_Category)%>% distinct()
ggplot(cust_Age,aes(Age,fill=Age))+
geom_bar()+
labs(title='Distribution of Customers Age')
##Q: if Age play a role in top5 selling products?
ggplot(top5, aes(Age))+
geom_bar(aes(fill=Product_ID))+
facet_wrap(~Product_ID,nrow=5)+
labs(title='Distribution of Customers Age of Top 5 Selling Product')
## info:There are some deviation in 26-35 category but not very clear.
##Q: age distribution when considering city?
ggplot(cust_Age, aes(as.factor(Age),fill=City_Category))+
geom_bar(aes(y=(..count..)/sum(..count..)*100))+
facet_wrap(~City_Category)+
theme(axis.text.x = element_text(angle=45, size=10, color='grey'))+
labs(y='Percentage', x='Age')
##info: city A customers are youngerCity
cust_City <-bf%>%select(User_ID,City_Category,Stay_In_Current_City_Years,Purchase)%>%group_by(User_ID,City_Category,Stay_In_Current_City_Years)%>%summarise(sumPurchase=sum(Purchase))
ggplot(cust_City,aes(City_Category))+
geom_bar(aes(fill=City_Category))+
labs(title='Distribution of city category customers live in')
##info: more than 50% of cutomers live in category C.
##actn: looking for the geographical and cultural features of these cities.
##Q: Does city_category has relation with Purchase amount of individual customers?
ggplot(cust_City,aes(City_Category,sumPurchase))+
geom_boxplot(aes(fill=City_Category),alpha=0.25)+
scale_y_continuous(label=comma)+
coord_cartesian(ylim=c(0,7000000))+
labs(title='Distribution of purchase amount of each City_Category')
##info: customers' purchase amount range is most wide in City_Category B, however City_Category A has more super shoppers.
##info: City_Category C's customers spend the least and are the least likely be a super shopper.
##Q: Does total purchase amount of each City_Category has something to say?
sumPurEachCat <- cust_City%>%group_by(City_Category)%>%summarise(sumPurchase=sum(sumPurchase))
ggplot(sumPurEachCat,aes(City_Category,sumPurchase))+
geom_bar(stat='identity',aes(fill=City_Category))+
scale_y_continuous(label=comma)+
labs(title='Total purchase amount of each City_Category')
##info: Eventhough City_Category B has fewer customers, it howevers contributes more revenue to this retailer.
##info: City_Category A has the fewest customers, around 1/3 the number of City_Category C, however City_Category A contributes almost the same amount of purchase amount. Maybe the super shoppers in City_Category A play a role!
##actn: On limited MKT budget, we need to focus on promotion to City_Category B's normal customers and City_Category A's super shoppers.
##Q: behaviors of customers in each city
cust_pur_city <- bf%>% group_by(User_ID,City_Category)%>%summarise(count=n(),amount=sum(Purchase))%>%arrange(desc(count))
table(cust_pur_city$City_Category[1:100])##
## A B
## 67 33ggplot(cust_pur_city,aes(User_ID,count))+
geom_point(aes(col=City_Category,alpha=0.1,position='jitter'))+
facet_wrap(~City_Category)+
theme(axis.title.x=element_blank(),axis.text.x=element_blank(),axis.ticks.x=element_blank())+
labs(title = "distribution of purchase items in each city")+
theme(legend.position='none')
##info: 67% of top 100 shoppers are from city A.
##info: Customers in city C buy very few times.
##info: City A has many super shoppers.
##actn: find out the reason why city C buy less amount.Stay_In_Current_City_Years
table(bf$Stay_In_Current_City_Years)##
## 0 1 2 3 4+
## 72725 189192 99459 93312 82889#transformming values into integer for more convenient when modelling
order=c('0'=0,'1'=1,'2'=2,'3'=3,'4'=4,'4+'=5)
bf$Stay_In_Current_City_Years <-
as.numeric(plyr::revalue(bf$Stay_In_Current_City_Years,order))
summary(bf$Stay_In_Current_City_Years)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 2.014 3.000 5.000##Q: Does Stay_In_Current_City_Years variable has some information?
ggplot(bf, aes(as.factor(Stay_In_Current_City_Years)))+
geom_bar(aes(fill=as.factor(Stay_In_Current_City_Years)))+
scale_fill_brewer(palette=15)+
labs(title = 'Customers Stay in Current City', y = 'Count', x = 'Stay in Current City', fill = 'Number of Years in Current City')
ggplot(bf, aes(City_Category,Stay_In_Current_City_Years))+
geom_boxplot()
bf_Stay_Year <- bf%>% select(City_Category,Stay_In_Current_City_Years)%>% group_by(City_Category)%>% summarise(mean=mean(Stay_In_Current_City_Years), median=median(Stay_In_Current_City_Years))
print(bf_Stay_Year)## # A tibble: 3 x 3
## City_Category mean median
## <chr> <dbl> <dbl>
## 1 A 1.96 2
## 2 B 2.03 2
## 3 C 2.04 2ggplot(bf,aes(as.factor(Stay_In_Current_City_Years),Purchase))+
geom_histogram(stat = 'summary', fun.y='mean')
cor(bf$Purchase,bf$Stay_In_Current_City_Years)## [1] 0.004750564## It seems that there is small info in this variable. We only know customers staying in current city for just one year is the biggest part.
stay_cities <- bf %>%
group_by(City_Category, Stay_In_Current_City_Years) %>% summarise(count=n()) %>% mutate(Percentage=count/sum(count)*100)
ggplot(stay_cities,aes(City_Category,count, fill= as.factor(Stay_In_Current_City_Years)))+
geom_bar(stat='identity')+scale_fill_brewer(palette = 2)+
labs(title = "City Category + Stay in Current City", y = "Total Count (Years)", x = "City", fill = "Stay Years")
####The heights of the bars commonly represent one of two things: either a count of cases in each group, or the values in a column of the data frame. By default, geom_bar uses stat="bin". This makes the height of each bar equal to the number of cases in each group, and it is incompatible with mapping values to the y aesthetic. If you want the heights of the bars to represent values in the data, use stat="identity" and map a value to the y aesthetic.Purchase
cust_purchase <- cust_City%>%ungroup()%>% select(User_ID,sumPurchase)
##Q: distribution of purchase amount?
ggplot(cust_purchase,aes(sumPurchase))+
geom_density(adjust=1)+
geom_vline(aes(xintercept=median(cust_purchase$sumPurchase)),col='blue',linetype='dotted')+
geom_vline(aes(xintercept=mean(cust_purchase$sumPurchase)),col='red',linetype='dashed')+
geom_text(aes(x=mean(cust_purchase$sumPurchase)),label=round(mean(cust_purchase$sumPurchase)), y=1.2e-06, color='red', angle=360, size=4, vjust=3, hjust=-.1)+
geom_text(aes(x=median(cust_purchase$sumPurchase)),label=round(median(cust_purchase$sumPurchase)), y=1.2e-06, col='blue', angle= 360, size=4, vjust=0, hjust=-.1)+
scale_x_continuous(name="Purchase Amount", limits=c(0,7500000),breaks=seq(0,7500000,by=1000000),expand=c(0,0), labels = comma)+
scale_y_continuous(name="Density ", limits=c(0,.00000125), labels=scientific, expand= c(0,0))
##info: very right skewed, the mean and median are deviated from the peak of probability.
##actn: focus on high value shopper?Marital status
mart_stat <- bf %>% select(User_ID,Marital_Status) %>% group_by(User_ID)%>% distinct()
#Q: What percentage of customers is married?
mean(mart_stat$Marital_Status)## [1] 0.4199627##info: arround 42% of customers has married.
##Q: relationship between marriage and city?
mart_city <- mart_stat%>%left_join(cust_City, by='User_ID') %>% group_by(City_Category, Marital_Status)%>% tally()
ggplot(mart_city, aes(City_Category,n, fill=as.factor(Marital_Status)))+
geom_bar(stat='identity', col='black')+
scale_fill_brewer(palette = 10)+
labs(title="City + Marital Status",y="Total Count (Shoppers)",x='City',fill="Marital Status")
##info: city A has a higher percentage of unmarriage, it also has more big shopper.
##Q: Is "stay in current city" correlated with marital status?
mart_stay <- mart_stat%>% left_join(cust_City,by='User_ID')%>%group_by(Stay_In_Current_City_Years,Marital_Status)%>%tally()%>%mutate(percent=n/sum(n)*100)
ggplot(mart_stay, aes(Stay_In_Current_City_Years,n, fill=as.factor(Marital_Status)))+
geom_bar(stat='identity')+
scale_fill_brewer(palette = 15)+
labs(y='Marital_Status', fill='Marital_Status')
Top shopper
top_shopper <- cust_pur_city%>% mutate(avg_amount=amount/count)%>%arrange(desc(avg_amount))
ggplot(top_shopper, aes(avg_amount))+
geom_density()+
geom_vline(xintercept=mean(top_shopper$avg_amount), linetype='dashed',col='red')+
geom_text(aes(x=mean(top_shopper$avg_amount), label= round(mean(top_shopper$avg_amount)), y=0.0002, col='red', size=5, hjust=0.8))+
theme(legend.position = 'none')
##Occupation
occu <- cust_pur_city[]%>%left_join(bf[,c(1,5)],by="User_ID")%>%distinct()
##Q: Which occupation buy the most?
occu_sum <- occu%>%group_by(Occupation)%>%summarise(amount=sum(amount), count=sum(count))%>%arrange(desc(amount))%>%mutate(percent=amount/sum(amount)*100)
ggplot(occu_sum) +
geom_bar(stat='identity', aes(as.factor(reorder(Occupation,-amount)),percent, fill=as.factor(Occupation)))+
theme(legend.position = 'none')+
scale_y_continuous(label=comma)+
labs(x='occupation', y='percent', title='total purchase amount by occupation')
sum(occu_sum$amount[1:5])/sum(occu_sum$amount)## [1] 0.5250289##info: occupation 4 buy the most and top 5 occupations contribute 52.5 % of the sales. Apriori Algorithm
Data Preprocessing
# Getting the dataset into the correct format
test <- bf%>%select(User_ID,Product_ID)%>%
# Selecting the columns we will need
group_by(User_ID)%>%
# Grouping by "User_ID"
arrange(User_ID)%>%
# Arranging by "User_ID"
mutate(id=row_number())
cust_prod <- bf%>%select(User_ID,Product_ID)%>%
# Selecting the columns we will need
group_by(User_ID)%>%
# Grouping by "User_ID"
arrange(User_ID)%>%
# Arranging by "User_ID"
mutate(id=row_number())%>%
# Defining a key column for each "Product_ID" and its corresponding "User_ID" (Must do this for spread() to work properly)
spread(User_ID,Product_ID)%>%
# Converting our dataset from tall to wide format, and grouping "Product_IDs" to their corresponding "User_ID"
t()
# Transposing the dataset from columns of "User_ID" to rows of "User_ID"
cust_prod <- cust_prod[-1,]
# Now we can remove the Id row we created earlier for spread() to work correctly.
str(cust_prod)## chr [1:5891, 1:1025] "P00069042" "P00285442" "P00193542" "P00184942" ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:5891] "1000001" "1000002" "1000003" "1000004" ...
## ..$ : NULL####最後剩下的檔案，, customer_id 為row_id, col_id 是product_bought_x
write.csv(cust_prod, file= 'customer_product.csv')
customer_product <- read.transactions('customer_product.csv', sep=',', rm.duplicates = TRUE)## distribution of transactions with duplicates:
## items
## 46 126 163 202 258 272 285 307 310 316 319 327 330 334 340
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 344 345 348 354 357 373 393 402 408 419 437 441 449 450 452
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 454 456 459 465 466 467 475 476 477 481 487 491 495 498 507
## 1 1 1 1 2 2 1 1 1 1 2 2 3 2 1
## 523 524 526 527 528 530 531 532 533 535 537 538 539 540 545
## 1 2 1 2 1 1 2 1 1 1 1 2 3 1 1
## 546 548 549 553 554 555 556 558 563 566 567 570 572 574 575
## 1 3 1 1 2 1 1 2 1 3 1 3 2 4 1
## 577 578 580 583 584 586 588 589 590 591 592 593 594 595 597
## 2 3 2 1 1 2 3 5 1 1 1 1 1 3 2
## 598 601 602 604 607 608 610 612 613 614 615 616 617 618 619
## 1 1 1 1 2 1 1 2 1 2 1 1 2 1 3
## 620 623 625 632 633 634 635 638 640 641 642 643 644 645 646
## 2 1 1 6 1 5 2 2 2 1 2 1 2 3 1
## 647 648 653 654 657 658 659 661 662 663 664 665 666 667 668
## 4 1 1 3 2 1 1 2 1 3 1 1 2 1 2
## 669 670 671 672 674 676 677 678 679 681 682 683 685 686 687
## 4 2 1 3 1 1 2 3 3 2 2 4 4 5 2
## 688 689 690 691 692 694 695 697 698 699 700 702 703 704 705
## 2 1 1 1 1 2 1 1 1 1 2 1 3 1 1
## 706 707 708 709 710 712 713 714 715 716 717 718 719 720 721
## 2 2 3 2 2 4 5 2 2 1 1 2 3 3 5
## 722 723 724 725 726 727 728 729 730 732 733 734 735 736 737
## 2 1 1 2 5 2 1 3 3 2 1 5 6 2 4
## 738 739 740 741 742 743 744 745 747 748 749 750 751 752 753
## 6 3 4 1 6 7 4 6 1 1 5 5 3 2 3
## 754 755 756 757 758 759 760 761 763 764 765 766 767 768 769
## 4 6 6 2 6 2 1 5 3 4 2 3 2 5 2
## 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
## 2 3 1 3 4 2 2 3 3 2 4 7 3 4 5
## 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799
## 5 3 3 6 5 5 2 3 5 3 8 5 5 9 3
## 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814
## 4 4 7 4 3 4 5 7 5 4 5 3 2 6 6
## 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829
## 3 11 5 10 6 6 4 7 7 2 5 4 7 5 5
## 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844
## 4 5 4 3 5 4 11 5 5 4 9 7 6 4 7
## 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859
## 9 11 4 6 10 6 10 7 12 16 11 8 7 4 12
## 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874
## 9 11 11 9 6 11 10 7 6 5 12 6 7 8 11
## 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889
## 9 9 8 7 5 4 15 13 12 8 4 6 12 15 13
## 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904
## 10 11 13 6 21 7 14 9 7 11 18 5 14 10 9
## 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919
## 19 15 10 17 18 23 8 19 15 12 18 21 17 12 11
## 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934
## 13 13 12 20 20 16 13 15 17 27 22 20 28 18 14
## 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949
## 20 20 20 14 22 30 23 23 21 20 25 19 30 31 30
## 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964
## 24 27 25 40 30 31 16 29 30 32 48 27 27 24 30
## 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979
## 26 35 43 30 51 49 40 41 36 32 36 38 43 41 42
## 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994
## 37 49 44 51 57 55 40 53 56 63 39 58 50 58 77
## 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009
## 74 72 72 84 74 66 77 85 93 79 94 118 122 104 121
## 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019
## 113 120 78 77 55 37 20 7 5 1####利用transaction func 讀取後的檔案，是以product_id 為col_name, customer_id 為row_id, 值為bool##checking if the result is right
topSellersummary(customer_product)## transactions as itemMatrix in sparse format with
## 5892 rows (elements/itemsets/transactions) and
## 10539 columns (items) and a density of 0.008768598
##
## most frequent items:
## P00265242 P00110742 P00025442 P00112142 P00057642 (Other)
## 1858 1591 1586 1539 1430 536489
##
## element (itemset/transaction) length distribution:
## sizes
## 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 1 5 7 20 37 55 77 78 120 113 121 104 122 118 94
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## 79 93 85 77 66 74 84 72 72 74 77 58 50 58 39
## 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 63 56 53 40 55 57 51 44 49 37 42 41 43 38 36
## 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
## 32 36 41 40 49 51 30 43 35 26 30 24 27 27 48
## 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 32 30 29 16 31 30 40 25 27 24 30 31 30 19 25
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## 20 21 23 23 30 22 14 20 20 20 14 18 28 20 22
## 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
## 27 17 15 13 16 20 20 12 13 13 11 12 17 21 18
## 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
## 12 15 19 8 23 18 17 10 15 19 9 10 14 5 18
## 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## 11 7 9 14 7 21 6 13 11 10 13 15 12 6 4
## 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155
## 8 12 13 15 4 5 7 8 9 9 11 8 7 6 12
## 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170
## 5 6 7 10 11 6 9 11 11 9 12 4 7 8 11
## 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
## 16 12 7 10 6 10 6 4 11 9 7 4 6 7 9
## 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
## 4 5 5 11 4 5 3 4 5 4 5 5 7 4 5
## 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
## 2 7 7 4 6 6 10 5 11 3 6 6 2 3 5
## 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230
## 4 5 7 5 4 3 4 7 4 4 3 9 5 5 8
## 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245
## 3 5 3 2 5 5 6 3 3 5 5 4 3 7 4
## 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
## 2 3 3 2 2 4 3 1 3 2 2 5 2 3 2
## 261 262 264 265 266 267 268 269 270 271 272 273 274 275 276
## 4 3 5 1 2 6 2 6 6 4 3 2 3 5 5
## 277 278 280 281 282 283 284 285 286 287 288 289 290 291 292
## 1 1 6 4 7 6 1 4 3 6 4 2 6 5 1
## 293 295 296 297 298 299 300 301 302 303 304 305 306 307 308
## 2 3 3 1 2 5 2 1 1 2 5 3 3 2 1
## 309 310 311 312 313 315 316 317 318 319 320 321 322 323 325
## 1 2 2 5 4 2 2 3 2 2 1 1 3 1 2
## 326 327 328 330 331 333 334 335 336 337 338 339 340 342 343
## 1 1 1 1 2 1 1 1 1 2 2 5 4 4 2
## 344 346 347 348 349 351 353 354 355 356 357 358 359 360 361
## 2 3 3 2 1 1 3 1 2 4 2 1 2 1 1
## 362 363 364 366 367 368 371 372 377 378 379 380 381 382 383
## 3 1 2 1 1 2 3 1 1 4 1 3 2 1 2
## 384 385 387 390 391 392 393 400 402 405 406 407 408 409 410
## 1 2 2 2 5 1 6 1 1 2 3 1 2 1 1
## 411 412 413 415 417 418 421 423 424 427 428 430 431 432 433
## 2 1 2 1 1 2 1 1 1 1 2 3 1 1 1
## 434 435 436 437 439 441 442 445 447 448 450 451 453 455 458
## 1 1 5 3 2 1 1 2 3 2 1 4 2 3 1
## 459 462 467 469 470 471 472 476 477 479 480 485 486 487 488
## 3 1 2 1 1 2 1 1 3 1 1 1 3 2 1
## 490 492 493 494 495 497 498 499 501 502 518 527 530 534 538
## 1 1 1 2 1 1 2 1 2 1 1 2 3 2 2
## 544 548 549 550 558 559 560 566 569 571 573 575 576 584 588
## 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1
## 606 617 623 632 652 668 671 677 680 681 685 691 695 698 706
## 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
## 709 715 718 740 753 767 823 862 899 979 1025 1026
## 1 1 1 1 1 1 1 1 1 1 1 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 26.00 54.00 92.41 115.00 1026.00
##
## includes extended item information - examples:
## labels
## 1 1000001
## 2 1000002
## 3 1000003####The element length distribution 是被購買的次數, eg. 被買7次的product 有5種
## info: the mean purchased time is 92.41, however the median is 54, right skewed.
itemFrequencyPlot(customer_product, topN=25)
?itemFrequencyPlotFit the model
The first value, lhs, corresponds to a grouping of items which the algorithm has pulled from the dataset.
The second value, rhs, corresponds to the value predicted by apriori to be purchased with items in the “lhs” category.
The third value, support is the number of transactions including that specific set of items divided by the total number of transactions. (As described earlier when we chose the parameters for Apriori.)
The fourth value, confidence is the % chance in which a rule will be upheld.
The fifth value, lift gives us the independance/dependence of a rule. It takes the confidence value and its relationship to the entire dataset into account.
The sixth and final value, count is the number of times a rule occured during the implementation of Apriori on our data.
rules <- apriori(data= customer_product, parameter= list(support=0.008, confidence= 0.8, maxtime= 0))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 0 0.008 1
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 47
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[10539 item(s), 5892 transaction(s)] done [0.17s].
## sorting and recoding items ... [2099 item(s)] done [0.01s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [16.76s].
## writing ... [7 rule(s)] done [0.39s].
## creating S4 object ... done [0.35s].rules2 <- apriori(data= customer_product, parameter= list(support=0.008, confidence= 0.75, maxtime= 0))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.75 0.1 1 none FALSE TRUE 0 0.008 1
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 47
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[10539 item(s), 5892 transaction(s)] done [0.15s].
## sorting and recoding items ... [2099 item(s)] done [0.01s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [15.66s].
## writing ... [171 rule(s)] done [0.40s].
## creating S4 object ... done [0.34s].#### maxtime = 0 will allow our algorithim to run until completion with no time limit
##support = 0.008, confidence= 0.8
inspect(sort(rules, by='lift'))## lhs rhs support confidence lift count
## [1] {P00032042,
## P00057642,
## P00102642,
## P00145042} => {P00270942} 0.008655804 0.8793103 4.540663 51
## [2] {P00025442,
## P00031042,
## P00034742,
## P00255842} => {P00145042} 0.008486083 0.8064516 3.433246 50
## [3] {P00003242,
## P00130742,
## P00237542} => {P00145042} 0.008316361 0.8032787 3.419738 49
## [4] {P00006942,
## P00251242,
## P00277642} => {P00145042} 0.009674134 0.8028169 3.417773 57
## [5] {P00034042,
## P00112442,
## P00112542} => {P00110742} 0.008146640 0.8135593 3.012880 48
## [6] {P00127642,
## P00165442,
## P00277442} => {P00110742} 0.008316361 0.8032787 2.974807 49
## [7] {P00051442,
## P00112142,
## P00112542,
## P00270942} => {P00110742} 0.008146640 0.8000000 2.962665 48plot(rules, method='graph')
##7 rules
##support = 0.008, confidence= 0.75
inspect(sort(rules2, by='lift'))## lhs rhs support confidence lift count
## [1] {P00221142,
## P00249642} => {P00103042} 0.008146640 0.7619048 8.030667 48
## [2] {P00002142,
## P00103042,
## P00147942} => {P00221442} 0.008146640 0.7500000 6.045144 48
## [3] {P00032042,
## P00057642,
## P00102642,
## P00145042} => {P00270942} 0.008655804 0.8793103 4.540663 51
## [4] {P00062842,
## P00127242,
## P00243942} => {P00044442} 0.008486083 0.7575758 4.061544 50
## [5] {P00030842,
## P00057942,
## P00355142} => {P00114942} 0.008486083 0.7936508 4.024260 50
## [6] {P00030842,
## P00147742,
## P00303342} => {P00044442} 0.008146640 0.7500000 4.020928 48
## [7] {P00030842,
## P00147742,
## P00270942} => {P00044442} 0.009674134 0.7500000 4.020928 57
## [8] {P00002142,
## P00030842,
## P0097242} => {P00114942} 0.008316361 0.7903226 4.007384 49
## [9] {P00028542,
## P00052842,
## P00329542} => {P00114942} 0.008146640 0.7868852 3.989955 48
## [10] {P00105142,
## P00127842,
## P00173042} => {P00112542} 0.008825526 0.7536232 3.989531 52
## [11] {P00003442,
## P00057542,
## P00277642} => {P00000142} 0.008825526 0.7647059 3.987298 52
## [12] {P00002142,
## P00030842,
## P00113142} => {P00114942} 0.008655804 0.7846154 3.978446 51
## [13] {P00057642,
## P00151742,
## P00199442} => {P00270942} 0.008995248 0.7681159 3.966467 53
## [14] {P00057642,
## P00102642,
## P00127742,
## P00145042} => {P00270942} 0.008146640 0.7619048 3.934393 48
## [15] {P00032042,
## P00057642,
## P00127642,
## P00145042} => {P00270942} 0.008146640 0.7619048 3.934393 48
## [16] {P00052842,
## P00334242,
## P00346242} => {P00114942} 0.009334691 0.7746479 3.927905 55
## [17] {P00052842,
## P00122442,
## P00151742} => {P00114942} 0.008146640 0.7741935 3.925601 48
## [18] {P00052842,
## P00122442,
## P00127842} => {P00114942} 0.008655804 0.7727273 3.918166 51
## [19] {P00040742,
## P00221442} => {P00270942} 0.008486083 0.7575758 3.912039 50
## [20] {P00057642,
## P00102642,
## P00127642,
## P00145042} => {P00270942} 0.008995248 0.7571429 3.909803 53
## [21] {P00143642,
## P00145042,
## P00303342} => {P00270942} 0.008316361 0.7538462 3.892780 49
## [22] {P00085942,
## P00103042,
## P00255842} => {P00270942} 0.008316361 0.7538462 3.892780 49
## [23] {P00046742,
## P00105142,
## P00140742,
## P00184942} => {P00270942} 0.008316361 0.7538462 3.892780 49
## [24] {P00001042,
## P00142142,
## P00243942} => {P00114942} 0.008316361 0.7656250 3.882154 49
## [25] {P00030842,
## P00113142,
## P00127642} => {P00114942} 0.008316361 0.7656250 3.882154 49
## [26] {P00151742,
## P00243942,
## P00295942} => {P00270942} 0.008146640 0.7500000 3.872918 48
## [27] {P00127342,
## P00326742} => {P00114942} 0.009843856 0.7631579 3.869644 58
## [28] {P00028542,
## P00244142} => {P00114942} 0.008146640 0.7619048 3.863290 48
## [29] {P00057942,
## P00147942,
## P00221542} => {P00034742} 0.008146640 0.7741935 3.839687 48
## [30] {P00052842,
## P00120042,
## P00122442} => {P00114942} 0.008316361 0.7538462 3.822428 49
## [31] {P00052842,
## P00100442,
## P00127842} => {P00114942} 0.008316361 0.7538462 3.822428 49
## [32] {P00034742,
## P00073642,
## P00145042} => {P00031042} 0.008995248 0.7571429 3.819423 53
## [33] {P00105142,
## P00169742,
## P00329542} => {P00114942} 0.008146640 0.7500000 3.802926 48
## [34] {P00059442,
## P00120042,
## P00122442} => {P00114942} 0.008146640 0.7500000 3.802926 48
## [35] {P00052842,
## P00073842,
## P00321742} => {P00114942} 0.008146640 0.7500000 3.802926 48
## [36] {P00112442,
## P00220342,
## P00251242} => {P00028842} 0.008146640 0.7500000 3.770478 48
## [37] {P00127642,
## P00153842,
## P0097242} => {P00117442} 0.008486083 0.7812500 3.751528 50
## [38] {P00101942,
## P00147942,
## P00258742} => {P00117442} 0.008146640 0.7619048 3.658633 48
## [39] {P00025442,
## P00031042,
## P00034742,
## P00255842} => {P00145042} 0.008486083 0.8064516 3.433246 50
## [40] {P00003242,
## P00130742,
## P00237542} => {P00145042} 0.008316361 0.8032787 3.419738 49
## [41] {P00006942,
## P00251242,
## P00277642} => {P00145042} 0.009674134 0.8028169 3.417773 57
## [42] {P00003942,
## P00057742,
## P00221442} => {P00145042} 0.008655804 0.7968750 3.392477 51
## [43] {P00006942,
## P00046742,
## P00277642} => {P00145042} 0.009164969 0.7941176 3.380738 54
## [44] {P00148642,
## P00226342,
## P00270942} => {P00145042} 0.008486083 0.7936508 3.378750 50
## [45] {P00006942,
## P00120042,
## P00277642} => {P00110942} 0.008146640 0.7619048 3.360137 48
## [46] {P00003242,
## P00111142,
## P00127842} => {P00145042} 0.011201629 0.7857143 3.344963 66
## [47] {P00116742,
## P00248142,
## P00271142} => {P00117942} 0.008655804 0.7611940 3.332062 51
## [48] {P00226342,
## P00244042,
## P00251242} => {P00145042} 0.008486083 0.7812500 3.325957 50
## [49] {P00070342,
## P00120042,
## P00243942} => {P00110942} 0.008825526 0.7536232 3.323614 52
## [50] {P00110942,
## P00154042,
## P00191442} => {P00145042} 0.008316361 0.7777778 3.311175 49
## [51] {P00046742,
## P00128242,
## P0097242} => {P00110942} 0.008655804 0.7500000 3.307635 51
## [52] {P00020342,
## P00182242,
## P00270942} => {P00145042} 0.008825526 0.7761194 3.304115 52
## [53] {P00110942,
## P00140742,
## P00145742} => {P00145042} 0.008146640 0.7741935 3.295916 48
## [54] {P00221142,
## P00249742} => {P00145042} 0.008486083 0.7692308 3.274789 50
## [55] {P00003242,
## P00127842,
## P00193542} => {P00145042} 0.008486083 0.7692308 3.274789 50
## [56] {P00100442,
## P00111142,
## P00147942} => {P00057642} 0.009164969 0.7941176 3.271987 54
## [57] {P00003242,
## P00145442,
## P00221442} => {P00145042} 0.009504413 0.7671233 3.265817 56
## [58] {P00125942,
## P00145442,
## P00221442} => {P00145042} 0.008316361 0.7656250 3.259438 49
## [59] {P00116842,
## P00147942,
## P00151742} => {P00058042} 0.009164969 0.7714286 3.255915 54
## [60] {P00057742,
## P00111942,
## P00270942} => {P00145042} 0.008825526 0.7647059 3.255525 52
## [61] {P00151742,
## P00222942} => {P00145042} 0.008146640 0.7619048 3.243600 48
## [62] {P00006942,
## P00221442,
## P00277642} => {P00145042} 0.008146640 0.7619048 3.243600 48
## [63] {P00057742,
## P00142142,
## P00199442} => {P00145042} 0.008146640 0.7619048 3.243600 48
## [64] {P00070042,
## P00117942,
## P00277642} => {P00145042} 0.011371351 0.7613636 3.241297 67
## [65] {P00110942,
## P00127942,
## P00289242} => {P00145042} 0.008655804 0.7611940 3.240575 51
## [66] {P00070042,
## P00110842,
## P00151742} => {P00145042} 0.008655804 0.7611940 3.240575 51
## [67] {P00062842,
## P00110742,
## P00110942,
## P00221442} => {P00046742} 0.008316361 0.7777778 3.234063 49
## [68] {P00003942,
## P00035842,
## P00251242} => {P00145042} 0.008995248 0.7571429 3.223328 53
## [69] {P00042142,
## P00057642,
## P00127942} => {P00046742} 0.009334691 0.7746479 3.221048 55
## [70] {P00031042,
## P00057642,
## P00370242} => {P00058042} 0.008146640 0.7619048 3.215718 48
## [71] {P00115142,
## P00147942,
## P00151742} => {P00058042} 0.008655804 0.7611940 3.212719 51
## [72] {P00221142,
## P00226342} => {P00145042} 0.008316361 0.7538462 3.209293 49
## [73] {P00070042,
## P00117942,
## P00161442} => {P00145042} 0.008316361 0.7538462 3.209293 49
## [74] {P00037142,
## P00154042,
## P00221442} => {P00145042} 0.008316361 0.7538462 3.209293 49
## [75] {P00003242,
## P00103042,
## P00251242} => {P00145042} 0.008825526 0.7536232 3.208344 52
## [76] {P00183342,
## P00226342,
## P00251242} => {P00145042} 0.009334691 0.7534247 3.207499 55
## [77] {P00057742,
## P00102642,
## P00103042} => {P00145042} 0.009334691 0.7534247 3.207499 55
## [78] {P00128942,
## P00144642,
## P00329542} => {P00057642} 0.010013578 0.7763158 3.198638 59
## [79] {P00244042,
## P00251842} => {P00046742} 0.008486083 0.7692308 3.198523 50
## [80] {P00208342,
## P00267542} => {P00145042} 0.008655804 0.7500000 3.192919 51
## [81] {P00020342,
## P00120042,
## P00270942} => {P00145042} 0.008146640 0.7500000 3.192919 48
## [82] {P00037142,
## P00201342,
## P00270942} => {P00145042} 0.008146640 0.7500000 3.192919 48
## [83] {P00006942,
## P00070042,
## P00265242} => {P00145042} 0.008146640 0.7500000 3.192919 48
## [84] {P00037142,
## P00110942,
## P00127842} => {P00145042} 0.009674134 0.7500000 3.192919 57
## [85] {P00110942,
## P00127942,
## P00323942} => {P00145042} 0.008146640 0.7500000 3.192919 48
## [86] {P00003942,
## P00035842,
## P00210042} => {P00145042} 0.008146640 0.7500000 3.192919 48
## [87] {P00003242,
## P00117442,
## P00142142} => {P00145042} 0.008655804 0.7500000 3.192919 51
## [88] {P00057742,
## P00100842,
## P00242742} => {P00145042} 0.008146640 0.7500000 3.192919 48
## [89] {P00057642,
## P00117442,
## P00221442,
## P00270942} => {P00145042} 0.008146640 0.7500000 3.192919 48
## [90] {P00110742,
## P00182742,
## P00183342} => {P00046742} 0.008825526 0.7647059 3.179709 52
## [91] {P00102642,
## P00111142,
## P00270942,
## P00329542} => {P00057642} 0.008486083 0.7692308 3.169446 50
## [92] {P00112542,
## P00130742,
## P00255842} => {P00058042} 0.008655804 0.7500000 3.165473 51
## [93] {P00006942,
## P00062842,
## P00242742} => {P00046742} 0.008655804 0.7611940 3.165106 51
## [94] {P00085942,
## P00183342,
## P0097242} => {P00057642} 0.008316361 0.7656250 3.154589 49
## [95] {P00058042,
## P00102642,
## P00127742} => {P00057642} 0.009843856 0.7631579 3.144424 58
## [96] {P00110942,
## P00130642,
## P00329542} => {P00057642} 0.008655804 0.7611940 3.136332 51
## [97] {P00100442,
## P00102642,
## P00145042,
## P00270942} => {P00057642} 0.008655804 0.7611940 3.136332 51
## [98] {P00169742,
## P00187442,
## P00242742} => {P00046742} 0.008316361 0.7538462 3.134553 49
## [99] {P00034042,
## P00052842,
## P00070042} => {P00046742} 0.008825526 0.7536232 3.133626 52
## [100] {P00110742,
## P00182342,
## P00241642} => {P00057642} 0.008486083 0.7575758 3.121424 50
## [101] {P00169742,
## P00183242,
## P00184942} => {P00046742} 0.008146640 0.7500000 3.118560 48
## [102] {P00062842,
## P00110742,
## P00110942,
## P00242742} => {P00046742} 0.009164969 0.7500000 3.118560 54
## [103] {P00003242,
## P00028842,
## P00237542,
## P00270942} => {P00057642} 0.008655804 0.7500000 3.090210 51
## [104] {P00034742,
## P00110942,
## P00145042,
## P00270942} => {P00057642} 0.008146640 0.7500000 3.090210 48
## [105] {P00034042,
## P00112442,
## P00112542} => {P00110742} 0.008146640 0.8135593 3.012880 48
## [106] {P00127642,
## P00165442,
## P00277442} => {P00110742} 0.008316361 0.8032787 2.974807 49
## [107] {P00051442,
## P00112142,
## P00112542,
## P00270942} => {P00110742} 0.008146640 0.8000000 2.962665 48
## [108] {P00051442,
## P00110742,
## P00117942,
## P00270942} => {P00112142} 0.008486083 0.7692308 2.944969 50
## [109] {P00128042,
## P00313542} => {P00112142} 0.008316361 0.7656250 2.931165 49
## [110] {P00046742,
## P00112542,
## P00325742} => {P00025442} 0.008655804 0.7846154 2.914851 51
## [111] {P00000142,
## P00070042,
## P00183242} => {P00112142} 0.008655804 0.7611940 2.914201 51
## [112] {P00070042,
## P00121142,
## P00129542} => {P00110742} 0.008146640 0.7868852 2.914097 48
## [113] {P00025442,
## P00057642,
## P00110842,
## P00140742} => {P00112142} 0.008486083 0.7575758 2.900349 50
## [114] {P00051442,
## P00192542,
## P00270942} => {P00112142} 0.008995248 0.7571429 2.898691 53
## [115] {P00025442,
## P00190142,
## P00221442} => {P00112142} 0.008995248 0.7571429 2.898691 53
## [116] {P00034042,
## P00244142} => {P00110742} 0.008316361 0.7777778 2.880369 49
## [117] {P00070042,
## P00113342,
## P00144642} => {P00110742} 0.008316361 0.7777778 2.880369 49
## [118] {P00046742,
## P00113142,
## P00325742} => {P00025442} 0.008146640 0.7741935 2.876134 48
## [119] {P00051442,
## P00110742,
## P00112542,
## P00270942} => {P00112142} 0.008146640 0.7500000 2.871345 48
## [120] {P00034042,
## P00111742,
## P0097242} => {P00110742} 0.008146640 0.7741935 2.867095 48
## [121] {P00000142,
## P00034042,
## P00277442} => {P00110742} 0.008146640 0.7741935 2.867095 48
## [122] {P00034042,
## P00118442,
## P0097242} => {P00110742} 0.008655804 0.7727273 2.861665 51
## [123] {P00052842,
## P00112142,
## P00173842} => {P00110742} 0.009674134 0.7702703 2.852566 57
## [124] {P00182742,
## P00278242} => {P00110742} 0.008486083 0.7692308 2.848716 50
## [125] {P00025442,
## P00034042,
## P00111742} => {P00110742} 0.008486083 0.7692308 2.848716 50
## [126] {P00021742,
## P00112142,
## P00147942} => {P00110742} 0.009504413 0.7671233 2.840912 56
## [127] {P00057642,
## P00105142,
## P00127342} => {P00025442} 0.011541073 0.7640449 2.838432 68
## [128] {P00000142,
## P00000642,
## P00112542} => {P00110742} 0.008316361 0.7656250 2.835363 49
## [129] {P00004742,
## P00034042,
## P0097242} => {P00110742} 0.008825526 0.7647059 2.831959 52
## [130] {P00046742,
## P00193542,
## P00325742} => {P00025442} 0.008146640 0.7619048 2.830481 48
## [131] {P00100442,
## P00110942,
## P00326742} => {P00025442} 0.008146640 0.7619048 2.830481 48
## [132] {P00110942,
## P00199442,
## P00326742} => {P00025442} 0.008146640 0.7619048 2.830481 48
## [133] {P00057942,
## P00073842,
## P00318742} => {P00110742} 0.009334691 0.7638889 2.828934 55
## [134] {P00025442,
## P00102642,
## P00105142,
## P00112542} => {P00110742} 0.009334691 0.7638889 2.828934 55
## [135] {P00021742,
## P00057942,
## P00112142} => {P00110742} 0.009843856 0.7631579 2.826226 58
## [136] {P00110942,
## P00184242,
## P00323942} => {P00110742} 0.008146640 0.7619048 2.821586 48
## [137] {P00028842,
## P00100442,
## P00316642} => {P00110742} 0.008146640 0.7619048 2.821586 48
## [138] {P00046742,
## P00112542,
## P00159442} => {P00110742} 0.008146640 0.7619048 2.821586 48
## [139] {P00034042,
## P00117242} => {P00110742} 0.008655804 0.7611940 2.818954 51
## [140] {P00034042,
## P00111142,
## P00128942} => {P00110742} 0.008655804 0.7611940 2.818954 51
## [141] {P00057942,
## P00070042,
## P00070342} => {P00110742} 0.009674134 0.7600000 2.814532 57
## [142] {P00000142,
## P00112542,
## P00216142} => {P00110742} 0.008486083 0.7575758 2.805554 50
## [143] {P00034042,
## P00057942,
## P00199442} => {P00110742} 0.008486083 0.7575758 2.805554 50
## [144] {P00003242,
## P00034042,
## P00057942} => {P00110742} 0.008486083 0.7575758 2.805554 50
## [145] {P00057942,
## P00112542,
## P00274242} => {P00110742} 0.008316361 0.7538462 2.791742 49
## [146] {P00053842,
## P00057942,
## P00277642} => {P00110742} 0.008316361 0.7538462 2.791742 49
## [147] {P00031042,
## P00105142,
## P00129542} => {P00110742} 0.008316361 0.7538462 2.791742 49
## [148] {P00025442,
## P00059442,
## P00111742,
## P00114942} => {P00110742} 0.008316361 0.7538462 2.791742 49
## [149] {P00025442,
## P00121642,
## P00161442} => {P00110742} 0.008825526 0.7536232 2.790916 52
## [150] {P00070342,
## P00144642,
## P00182242} => {P00110742} 0.008825526 0.7536232 2.790916 52
## [151] {P00111142,
## P00154042,
## P00275842} => {P00110742} 0.009334691 0.7534247 2.790181 55
## [152] {P00003242,
## P00025442,
## P00034042} => {P00110742} 0.009334691 0.7534247 2.790181 55
## [153] {P00057942,
## P00105142,
## P00182242} => {P00110742} 0.010862186 0.7529412 2.788391 64
## [154] {P00051442,
## P00059442,
## P00110742,
## P00111742} => {P00025442} 0.008146640 0.7500000 2.786255 48
## [155] {P00274142,
## P00316642} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [156] {P00046742,
## P00057642,
## P00222942} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [157] {P00046742,
## P00145742,
## P0097242} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [158] {P00010742,
## P00053842,
## P00057942} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [159] {P00034042,
## P00105142,
## P00121642} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [160] {P00057942,
## P00070342,
## P00173842} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [161] {P00057942,
## P00101942,
## P00112542} => {P00110742} 0.009164969 0.7500000 2.777498 54
## [162] {P00070042,
## P00112542,
## P00142142} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [163] {P00057642,
## P00057942,
## P00184942,
## P00270942} => {P00110742} 0.008655804 0.7500000 2.777498 51
## [164] {P00046742,
## P00073842,
## P00110942,
## P00112542} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [165] {P00051442,
## P00111142,
## P00112142,
## P00270942} => {P00110742} 0.008146640 0.7500000 2.777498 48
## [166] {P00028842,
## P00212942,
## P00294542} => {P00265242} 0.008146640 0.7741935 2.455085 48
## [167] {P00046742,
## P00145442,
## P00294542} => {P00265242} 0.008146640 0.7619048 2.416116 48
## [168] {P00102342,
## P00248742} => {P00265242} 0.009164969 0.7605634 2.411862 54
## [169] {P00057642,
## P00213242,
## P00278642} => {P00265242} 0.009164969 0.7605634 2.411862 54
## [170] {P00028842,
## P00031042,
## P00234842} => {P00265242} 0.008486083 0.7575758 2.402388 50
## [171] {P00003942,
## P00010742,
## P00182742} => {P00265242} 0.008146640 0.7500000 2.378364 48plot(rules2, method='graph', max=25)
plot(rules2, method='grouped', max=25)
##171 rules
##interpretation: customer who bought P00221142,P00249642 bought P00221142,P00249642 76.19% of the time, given a support of 0.008.
#The size of the bubbles represents the support value of the rule and the fill/color represents the lift.